Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Graph Attention Network× | XGBoost× | |
|---|---|---|
| Vakgebied≠ | Deep learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2018 | 2016 |
| Grondlegger≠ | Veličković, P. et al. | Chen, T. & Guestrin, C. |
| Type≠ | Graph neural network (attention-based) | Ensemble (gradient-boosted decision trees) |
| Oorspronkelijke bron≠ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Aliassen≠ | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network | XGBoost, extreme gradient boosting, scalable tree boosting |
| Verwant≠ | 4 | 5 |
| Samenvatting≠ | The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN). | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateGegevensset ↗ |
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